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Using Gate and Key Systems to Engage Players with Varied Motivation Profiles
No full textThis study explores how literal gates and skill-based gates in level design can influence player behavior and align with different motivation profiles based on Nick Yee\u27s Gamer Motivation Model [1]. The researcher created a single-player video game level using Dying Light 2, called Electric Revival, to explore the appeal of literal gates and skill-based gates. The researcher expects that different types of players will exhibit distinct preferences for specific types of gates based on their motivation profiles, which align with their primary motivations. The researcher recruited 14 playtesters, of varying motivation types, to play the level and share their perceptions on the implemented gating mechanisms
Data Encoding, Compilation, and Algorithms for Quantum Machine Learning
No full textQuantum computing enables new approaches to data processing, especially in quantum machine learning. Unlike classical systems, quantum data must be synthesized through operations and can exist in superposition. Encoding choices affect efficiency, noise resilience, and trainability—key factors in quantum machine learning models. This dissertation enhances quantum data encodings by extending quantum read-only memory (QROM) beyond binary representations, improving efficiency and parallelism. It introduces new compilation methods for quantum random number generators (QRNGs), supporting non-parametric distributions for post-quantum cryptography. Additionally, it explores Cayley graph-based encodings to extract spectral features for quantum machine learning
Topology Optimization Based on Micropolar Elasticity and Enhanced by Machine Learning: Structure Generation and Material Design
No full textThis work presents a novel topology optimization (TO) framework that integrates the micropolar elasticity theory with machine learning (ML) techniques to design high-performance structures and metamaterials. Traditional TO approaches rooted in classical elasticity neglect microstructural effects such as size-dependent behaviors and microrotations, which limits their accuracy for advanced materials (e.g., composites and metamaterials). To address this limitation, a new TO model based on micropolar (Cosserat) elasticity is developed, which introduces the rotational degrees of freedom and the associated couple stresses to more accurately capture microstructure-dependent mechanical responses.
The framework is further enhanced with ML algorithms – including feedforward neural networks (FFNN), convolutional neural networks (CNN), and generative adversarial networks (GAN) – to accelerate the optimization process. By training these models on intermediate designs from iterative TO, the ML-assisted approach can predict near-optimal material layouts with greatly reduced computational effort. Compared to conventional methods, the integrated approach achieves an 80–85% reduction in iteration count, about 80% faster convergence, and approximately 70% lower computational energy consumption, while maintaining a high level of accuracy (with a root-mean-square error ≤ 0.007).
The proposed methodology is validated through both 2D and 3D structural examples under diverse loading conditions. Results show that incorporating micropolar parameters (such as a coupling coefficient and a characteristic length) into the TO significantly enhances structural stiffness – improvements of up to 18.5% are observed – by enabling better load distribution and increased bending resistance. For mechanical metamaterials, the framework optimizes periodic structures for target properties (e.g., bulk or shear modulus and micropolar coupling effects), with the ML models effectively capturing design trends under periodic boundary conditions. In case studies, the deep learning-based predictors (CNN and GAN) outperformed the FFNN in accurately generating spatially complex optimal topologies.
Overall, this work bridges advanced continuum mechanics with data-driven optimization techniques, offering a robust tool for designing next-generation materials and lightweight structures in fields such as aerospace, automotive, and biomedical engineering. The findings demonstrate the potential of combining physics-based modeling with machine learning to efficiently solve high-resolution topology optimization problems that were previously computationally prohibitive
The Prevalence and Impact of Discourse in Social Media Networks: The 2024 Presidential Election
No full textAbstract. Social Media platforms serve as central hubs for global discourse, where political dialogue is widely shared and echoed. This exchange shapes civic participation and influences electoral outcomes, often with both intended and unintended consequences. In its inception, social media platforms served as message boards for the masses, yet manipulation and exploiting of systems via bot usage has made platforms susceptible to outside forces. Thus, false narratives and an artificial sense of consensus are endemically augmented. As for the 2016 and 2020 U.S. presidential elections, it is important to investigate the prevalence of evolved bot activity in both political and social media discourse. This research\u27s goal is to investigate how social media influences elections and democratic processes, with a focus on disinformation, the shaping of public discourse, and the influencing of political outcomes. This study examines how systematic and automated accounts artificially shape national engagement within the United States by leveraging machine learning models such as Random Forest, XGBoost, and Botometer for bot detection. The analysis utilizes public, private, and web-scraped datasets from social media platforms, including Facebook, Reddit, and X (sourced from Twibot-22, Kaggle, and independent web scraping). Results will be evaluated not only for bot detection accuracy and prevalence but also for their broader implications on online discourse, polarization, and information dissemination leading up to the 2024 presidential election. Ethical considerations include user anonymity and compliance with platform policies. This research aims to provide insights into the evolving role of social media in shaping public opinion and electoral influence
Reframe: A New Interpretive Framework for Non-Governmental Entity Responsibility in Outer Space
No full textThe mainstream interpretation of State responsibility for Non-Governmental Entities (NGEs) with respect to their commercial operations in outer space may hinder the further development of the commercial space industry. Specifically, that interpretation produces broad responsibility for nation-states (States) under international law and derivative consequences for States and NGEs, but the international space community might avoid the otherwise harsh practical consequences that are likely to hamper the continued growth of commercial activities by NGEs in outer space by reexamining and reinterpreting a few aspects of existing law. This article (1) examines mainstream interpretations of State responsibility for NGEs with respect to their commercial operations in outer space; (2) embraces Curtis Schmeichel’s work and his rejection of “national activities” as an all-encompassing concept that renders States responsible for nearly all of their NGEs’ activities and extends his contextual analysis to the remainder of Article VI of the Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, including the Moon and Other Celestial Bodies (Outer Space Treaty or OST); and (3) proposes a reinterpretation of present international space law concerning State responsibility by arguing that the “authorization and continuing supervision” requirement with respect to NGEs, as set forth in the second sentence of OST Article VI, is independent of carrying out activities “in conformity with the provisions set forth in the [OST].” Pursuant to that reinterpretation, each State that is a party to the OST and the 1972 Liability Convention on International Liability for Damage Caused by Space Objects (Liability Convention), recognizing its liberation from the need to ensure its NGEs’ conformity with the OST but respecting the remaining potential international liability for outer space activities by its NGEs (which might flow therefrom pursuant to Article VII of the OST and Article III of the Liability Convention, respectively), might endeavor to minimize the likelihood that such activities result in injurious consequences by regulating such activity. Accordingly, a State’s recognition of its ex post international liability for its NGEs’ injurious conduct in outer space may result in its ex ante adoption of authorization regimes and supervisory regulations specifically designed to limit that liability without the burden of, or need to, ensure such NGEs’ adherence to the provisions of the OST when they are not engaged in national activities. As private sector activities in outer space expand, such an approach to the OST’s Article VI should reduce the regulatory load States must shoulder to comply with their duties to authorize and continually supervise NGEs’ non-national activities in outer space while correspondingly increasing the discretion such States may exercise in fulfilling those duties.
In summation, this article proposes a drastic narrowing of States’ responsibility for NGEs’ activities in outer space, which should enable States to reduce their supervisory burden or otherwise tailor their supervisory role for the non-national activities of their NGEs’ in outer space in a manner consistent with such States’ own respective interests and agendas free of the need to ensure those NGEs’ “compliance” with the OST. States’ liability for NGEs’ actions in outer space, however, would not be altered by the reinterpretation of the OST proposed in this Article. But a State’s liability for its NGEs’ activities in outer space is based on comparative fault. States who have laws and regulations governing NGEs’ activities in outer space could argue that they should shoulder less comparative fault than States who lack such laws and regulations; this, coupled with the “stickiness” of liability under international space law (i.e., a launching state retains liability for its space objects indefinitely), should mitigate “race to the bottom” fears for States vis-a-vis their NGEs’ activities in outer space
Three Essays in International Trade and Macroeconomics
No full textThis dissertation examines the impact of international trade and trade policy on key economic outcomes, including income and productivity. The first chapter investigates the role of trade-induced technology diffusion in shaping global income inequality. When bilateral trade facilitates technology transfer, such diffusion can account for up to 85% of the income gap across countries. The second chapter focuses on the determinants of international trade in services. It highlights a significant link between service and manufacturing trade, conditional on the presence of foreign direct investment (FDI) relationships between trading partners. The third chapter evaluates the 2020 U.S. presidential election, aiming to identify the factors that contributed to Donald Trump’s electoral defeat. The analysis finds that the expansion of health insurance coverage played the most significant role in explaining his loss
Portfolio Dynamics and the Supply of Safe Securities
No full textI study dynamic portfolio rebalancing in Collateralized Loan Obligations (CLOs) by developing an industry equilibrium model of nonbank lending, in which CLOs and loan funds arise endogenously in response to a premium for safe securities. When loans deteriorate after issuance, CLOs rebalance their portfolios to maintain collateral quality, which protects senior tranches at the expense of equity investors. This self-healing mechanism lowers CLOs\u27 ex-ante funding costs by enabling the issuance of larger safe tranches. As more lenders operate CLOs, their portfolio rebalancing generates greater non-fundamental price pressures, incentivizing other lenders to operate loan funds. Overall, portfolio dynamics facilitate risk sharing across nonbank lenders and increase both total lending and the supply of safe securities relative to static portfolios
Farming with data: tracing critical tensions using data science for food justice
No full textIn this manuscript, we explore the intersection of artificial intelligence (AI) and equitable learning in higher education, focusing on data science as a subset of AI and social justice as the core theme of equity. Our investigation sheds light on the nuanced tensions inherent in employing data science for social justice. Rooted in situated perspectives of learning and consequential learning, our study employs an instrumental case-study methodology and analysis techniques from interaction and conversation analysis. Collaborating with three undergraduate students and an urban farm, the students used data science practices to highlight inequities surrounding food justice and access to food. Our findings reveal three key tensions: (1) the undergraduates\u27 discourse on simplicity versus complexity in utilizing data science for social justice, (2) the challenges of balancing data science with social justice imperatives, and (3) the successful application of data science by the students in their food justice project, culminating in a presentation of their findings to the farm\u27s director. We conclude by discussing implications for research and the use of data science in social justice project
Systemic Risk and the Social Contract
No full textThe stability of the constitutional order turns, in part, on a stable economy and reliable advances in technology. Political order cannot withstand economic collapse or a massive technological failure. Such crises chip away at the collective will to adhere to the social contract because they indicate the government may not have the capacity to uphold its end of the bargain—protecting individual liberty from broad threats. “Unprecedented” economic downturns, however, have a precedent of emerging from the very deliberate decision of some actors to pursue extremely risky behavior in their self-interest at the expense of the public. Societal disruption from over-dependence on specific technology is also the product of known factors. Governments have no excuse, then, for serially allowing the risky behavior of a few to imperil the political order upon which the many rely for liberty, opportunity, and stability. This is not to say that detecting and stemming such risky behavior before a crisis occurs is easy. It is not. Ignorance, though, can no longer serve as an excuse for governments not taking more seriously systemic risks to the economy and, by extension, the political order. The transition from the Articles of Confederation to the Constitution and the text of early state constitutions make clear that both levels of government must proactively and successfully mitigate systemic risks. Overreliance on AI could cause severe economic and technological chaos upon some failure. If governments allow such risks to go unaddressed, they will be in violation of the social contract—a fact that mandates that state and federal officials do more than simply try to adjust old regulatory frameworks, such as antitrust law, to this novel risk. A few policy solutions could demonstrate a good-faith effort to shield people if AI does indeed go south. None of the solutions will alone mitigate the risks of excessive reliance on a few AI labs. First, the state and federal governments can insist on a diversified AI portfolio in their own procurement practices. The government’s purchasing power can induce competition in the AI space-chipping away at the dominance of the first scalers, such as OpenAI, in the field. Knowledge of lucrative contracts with the federal government could be the seed of several startups that grow to become key players in the market. Second, state legislatures and Congress should explore imposing an insurance requirement on the largest AI labs. If you build it and break it, then you should pay for it. This approach has historical and modern precedent. As far back as World War I, the government took active steps to ensure against worst-case economic scenarios during turbulent times. Other strategies abound and should be proposed and explored. The key is that the government should not sit on the sidelines and allow further risks to develop
Race to Robustness: Adaptation to Globalization in Authoritarian Regimes of the Middle East
No full textThe Middle East has been notably absent from the wave of democratization and failed to adapt to globalization since the 1990s. In the Middle Eastern countries, the economy has largely stagnated and the political system has remained authoritarian despite popular revolts during the Arab Spring. Earlier literature often emphasized the cultural and religious differences between the Middle East and the rest of the world. However, the Arab Spring proved that the Middle East could be a part of the democratization wave. More recent literature attempts to explain this surge of public response to the regime and explores how the authoritarian regime resists and/ or adapts to the push of globalization. It highlights weak private sectors, intra-state regional fragmentation, and trade protectionism. Authoritarian leaders use concentrated political power, economic control, and cultural rhetoric to implement policies. We explore how to explain the different responses of authoritarian regimes in the Middle East to globalization by comparing past explanations in the literature. We illustrate patterns of adaptation to globalization by focusing on institutionalized vested interests involving military and civil society. We primarily compare Egypt and Tunisia, but also discuss Saudi Arabia to explore the implications of oil endowment and Lebanon to explore the implications of fractured democracy